SmartCar: detecting driver stress

  • Healey J
  • Picard R
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Abstract

Smart physiological sensors embedded in an automobile afford a
novel opportunity to capture naturally occurring episodes of driver
stress. In a series of ten ninety minute drives on public roads and
highways, ECG, EMG, respiration and skin conductance sensors were used
to measure the autonomic nervous system activation. The signals were
digitized in real time and stored on the SmartCar's Pentium class
computer. Each drive followed a pre-specified route through fifteen
different events, from which four stress level categories were created
according to the results of the subjects self report questionnaires. In
total, 545 one minute segments were classified. A linear discriminant
function was used to rank each feature individually based on the
recognition performance, and a sequential forward floating selection
algorithm was used to find an optimal set of features for recognizing
patterns of driver stress. Using multiple features improved performance
significantly over the best single feature performance

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Authors

  • J. Healey

  • R. Picard

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